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With increasing wind installations, there is a need to analyse wind generation variability in detail. This paper applies the reanalysis approach for modelling the variability; however, with two important additions. Firstly, high-resolution microscale data is combined with mesoscale reanalysis time series to model local variability in wind. Secondly, as there are often missing technical parameters in large-scale wind power plant datasets, machine learning is used to estimate the missing values. It is shown that such detailed modelling leads to more accurate simulations than a baseline model when compared to historical data from multiple European countries. In addition, applicability of the methodology for analysing future scenarios with changing wind installations is demonstrated.
|Journal||Electric Power Systems Research|
|Number of pages||7|
|Publication status||Published - 2021|
|Event||XXI Power Systems Computation Conference - ONLINE EVENT, Porto, Portugal|
Duration: 29 Jun 2020 → 3 Jul 2020
Conference number: 21
|Conference||XXI Power Systems Computation Conference|
|Period||29/06/2020 → 03/07/2020|
- Random forest
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01/07/2019 → 30/09/2022